app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ajibawa-2023/Young-Children-Storyteller-Mistral-7B"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Try 8-bit quantization to reduce memory usage
try:
    from transformers import BitsAndBytesConfig
    bnb_config = BitsAndBytesConfig(load_in_8bit=True)
    model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", quantization_config=bnb_config)
except:
    model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)

def generate_story(prompt):
    outputs = generator(prompt, max_length=150, do_sample=True, temperature=0.8, top_p=0.9)
    return outputs[0]['generated_text']

iface = gr.Interface(
    fn=generate_story,
    inputs=gr.Textbox(lines=3, placeholder="Enter your story prompt here..."),
    outputs="text",
    title="Young Children Storyteller",
    description="Generate children's stories using Mistral 7B"
)

if __name__ == "__main__":
    iface.launch()